Packages Needed

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(DT)
## Warning: package 'DT' was built under R version 4.4.3

Read CSV File

LB_Stats <- read.csv("LB_Rankings.csv") %>%
  arrange(rank) %>%
  mutate(pos_rank_bef = row_number())
datatable(LB_Stats)

Get Mean Values

mean(LB_Stats$pass_grade)
## [1] 72.79615
mean(LB_Stats$run_grade)
## [1] 77.35
mean(LB_Stats$cov_grade)
## [1] 66.81538
mean(LB_Stats$missp)
## [1] 10.91923
mean(LB_Stats$passer_rating)
## [1] 95.49231

Create Values Function

get_lb_values <- function(input_df) {
  df_lb_copy <- input_df %>% mutate(
    pass_grade = round(pmax(pmin((pass_grade-60) / 2.5, 10), 0), 2),  # 60-85, mean 72.5
    run_grade = round(pmax(pmin((run_grade-65) / 2, 10), 0), 2), # 65-85, mean 75
    cov_grade = round(pmax(pmin((cov_grade-50) / 3, 10), 0), 2), # 50-80, mean 65
    missp = round(pmax(pmin(((100-missp)-83) / 1.2, 10), 0), 2), # 17-5, mean 11
    passer_rating = round(pmax(pmin(((200-passer_rating)-80) / 5, 10), 0), 2), # 120-70, mean 95
  )
  
  return(df_lb_copy)
}

Create Final Dataset

new_stats_lb <- get_lb_values(LB_Stats) %>% 
  mutate(total = rowSums(select(., -player, -adp, -rank, -team, -pos_rank_bef))) %>% 
  arrange(-total) %>% 
  mutate(pos = "LB", pos_rank_aft = row_number(), pos_rank_diff = pos_rank_bef-pos_rank_aft) %>%
  select(player, pass_grade, run_grade, cov_grade, missp, passer_rating, total, rank, pos, team, pos_rank_bef, pos_rank_aft, pos_rank_diff)
datatable(new_stats_lb)

Get Total Value and Rank

get the dataset that only contains the total and the ranks

lb_stats_total <- new_stats_lb %>%
  select(player, total, rank, pos, team, pos_rank_bef, pos_rank_aft, pos_rank_diff)
datatable(lb_stats_total)

Download Final Rankings

write.csv(lb_stats_total, "lb_stats_total.csv", row.names = FALSE)